Interactive-aware clustering of stable states

1. A computer-implemented method for an analysis of genetic disease progression, said method comprising:

receiving data of a set of molecular statuses;

providing a dynamic prediction model of molecular interactions over time;

determining said molecular statuses of said set over time using said dynamic prediction model; and

clustering said determined molecular statuses by applying an interaction-aware metric for said analysis of said genetic disease progression, the interaction-aware metric including a framework of determining distances considering a network topology of interacting genes, wherein the interaction-aware metric that determines the distances considers multidimensional molecular interaction representing at least an influence of a concentration of a gene on another gene, wherein the clustering considers underlying molecular interactions in addition to distances between the molecular statuses.

2. The method according to claim 1 , wherein said dynamic prediction model is selected out of said group comprising a Boolean model, a set of ordinary differential equations, a set of stochastic differential equations, a set of partial differential equations.

3. The method according to claim 1 , wherein said set of molecular statuses is represented by measurement values of a patient, a cell line or drug perturbations.

4. The method according to claim 1 , wherein said dynamic prediction model and related parameters are downloadable from a remote database.

5. The method according to claim 1 , wherein said interaction-aware metric is at least in parts based on a Laplacian matrix, a deformed Laplacian matrix, a symmetric Laplacian matrix, a random walk Laplacian matrix and/or a Vicus matrix.

6. The method according to claim 1 , wherein said analysis of said genetic disease progression comprises

determining a personalized therapy.

7. The method according to claim 1 , also comprising

determining a disease sub-type based on said clustering.

8. The method according to claim 1 , wherein said clustering is based on at least one algorithm selected out of said group k-means, DBSCAN, and hierarchical clustering.

9. The method according to claim 1 , wherein as distance measure during said clustering a Manhattan distance or an Euclidean distance is determined.

10. The method according to claim 9 , wherein said distance determination is also a function of said interaction-aware metric.

11. A disease analysis system for an analysis of genetic disease progression, said system comprising

a receiving unit adapted for receiving data about a set of molecular statuses;

a prediction module adapted for a dynamic prediction model of molecular interactions over time;

a determination unit adapted for determining said molecular statuses of said set over time using said dynamic prediction model; and

a clustering module adapted for clustering said determined molecular statuses by applying an interaction-aware metric to analyze genetic disease progression, the interaction-aware metric including a framework of determining distances considering a network topology of interacting genes, wherein the interaction-aware metric that determines the distances considers multidimensional molecular interaction representing at least an influence of a concentration of a gene on another gene, wherein the clustering considers underlying molecular interactions in addition to distances between the molecular statuses.

12. The system according to claim 11 , wherein said dynamic prediction model is selected out of said group comprising a Boolean model, a set of ordinary differential equations, a set of stochastic differential equations, a set of partial differential equations.

13. The system according to claim 11 , wherein said data of said set of molecular statuses is represented by measurement values of a patient, a cell line or drug perturbations.

14. The system according to claim 11 , also comprising

a download module adapted for downloading said dynamic prediction model and related parameters from a remote database.

15. The system according to claim 11 , wherein said interaction-aware metric is at least in parts based on a Laplacian matrix, a deformed Laplacian matrix, a symmetric Laplacian matrix, a random walk Laplacian matrix and/or a Vicus matrix.

16. The system according to claim 11 , also comprising

personalization determination module adapted for a determination of a personalized therapy as part of said analysis of said genetic disease progression.

17. The system according to claim 11 , wherein said clustering module unit is also adapted for

determining a disease sub-type.

18. The system according to claim 11 , wherein said clustering module unit is adapted for performing at least one algorithm selected out of said group k-means, DBSCAN, and hierarchical clustering.

19. The system according to claim 11 , wherein as distance measure during said clustering a Manhattan distance or an Euclidean distance is determined.

20. A computer program product for an efficient analysis of genetic disease progression, said computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, said program instructions being executable by a computer to cause said computer to:

receive data about a set of molecular statuses;

provide a dynamic prediction model of molecular interactions over time;

determine said molecular statuses of said set over time using said dynamic prediction model; and

cluster said determined molecular statuses by applying an interaction-aware metric to analyze genetic disease progression, the interaction-aware metric including a framework of determining distances considering a network topology of interacting genes, wherein the interaction-aware metric that determines the distances considers multidimensional molecular interaction representing at least an influence of a concentration of a gene on another gene, wherein clustering considers underlying molecular interactions in addition to distances between the molecular statuses.